Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method

Siqi Gong,Yanfeng Wang,Huijie Ma,Guangrong Teng,Jiantao Lu,Shunming Li

doi:10.3390/app12031414

Siqi Gong, Yanfeng Wang + Show 4 more

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https://doi.org/10.3390/app12031414

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Abstract

In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD for denoising is mainly to remove singular components (SCs) with small singular value (SV), which ignores the weak signals buried in strong noise. Aiming to extract the weak signals in strong noise, this paper proposed a method of selecting SCs by the correntropy-induced metric (CIM). Then, the frequency components of characteristic signals can be found through cyclic correntropy spectrum (CCES) which is the extension of the correntropy (CE). The proposed method firstly merges the signals collected by the two channels, secondly uses the principal components analysis (PCA) method to reduce the dimensionality, thirdly uses the singular value decomposition method to decompose the signal, fourthly calculates the CIM value to determine the selected singular components for construction, and finally uses the cyclic correntropy spectrum displaying the characteristics of the reconstructed signal. The experimental results show that the proposed method has a good effect on feature extraction.

Highlights

Singular value decomposition (SVD) is a simple and effective method for denoising [1]
Inspired by previous research on SVD, Zhao Ming and Jia Xiaodong [5] proposed a novel strategy for signal denoising using reweighted SVD
The traditional SVD denoising method based on energy is not suitable for weak faults detection [6], so it is necessary to find other methods for selecting singular value (SV), such as information-based methods [7]

Summary

Introduction

Singular value decomposition (SVD) is a simple and effective method for denoising [1]. Zhao Xuezhi et al conducted an in-depth study on the decomposition results and noise effects of SVD [4] This team proposed to use the difference spectrum to select the effective singular components (SCs). PCA is a multivariate statistical method that converts multiple variables into a few principal components (i.e., comprehensive variables) through dimensionality reduction techniques. When the research question involves multiple variables and the correlation between the variables are obvious, that is, when the information contained overlaps, the method of principal component analysis can be considered. The cumulative contribution rate is calculated by dividing the corresponding first k eigenvalues matrix by the sum of all eigenvalues of the covariance When it reaches more than 95% according to the confidence level in probability statistics (90%, 99%, etc., can be selected according to actual needs), the selected principal components can be considered effective

Theory of Singular Value Decomposition

Presentation of the Proposed Method

The Role of Cyclic Correntropy Spectrum

Findings

Conclusions